Methods and Compositions for Assessing Patients with Preeclampsia-Related Conditions Using MicroRNA

ABSTRACT

The invention is directed to methods and compositions for collecting a non-placental biological samples of cells and quantifying and comparing levels of expression of microRNAs to characterize a preeclampsia-related condition. The samples may be collected before or after an intervention or may be collected over a period of time. One of the samples may be a control sample. Patients may then be treated according to their response.

RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.13/284,739, filed Oct. 28, 2011, PCT/US12/61994, filed Oct. 25, 2012,and U.S. Provisional Patent Application Ser. No. 61/767,669, filed Feb.21, 2013, all of which are incorporated by reference in their entirety.

FIELD OF THE PRESENT INVENTION

This disclosure generally relates to diagnosis and treatment ofreproductive disorders and more specifically to methods and compositionsfor characterizing individuals or groups of individuals using patternsof expression of one or more microRNA sequences.

BACKGROUND OF THE INVENTION

Preeclampsia and related conditions consume a major part of themanagement of women who are pregnant or who plan to become pregnant.Loss of pregnancy, both early and late contribute to the total burden ofpreeclampsia-related disorders on couples desiring children. Theeconomic impact of the disorder when manifested in the later part ofpregnancy is particularly severe. At present, management of preeclampsialargely involves control of maternal symptoms and early delivery. Earlydiagnosis with appropriate treatment offers some hope of prevention.

Clinical symptoms of preeclampsia are largely experienced in the thirdtrimester. It may be subdivided into an early and more severe formmanifesting prior to 34 weeks gestation and a more mild form manifestinglater. Defined as the appearance of hypertension and proteinuria after20 weeks gestation, the condition is recognized as a manifestation ofendothelial dysfunction in various maternal organs. The most common ofthese is endothelial dysfunction within the arterioles of the renalglomerulus. Election microscopy demonstrates endothelial swelling and aloss of fenestrations that are requisite to optimal glomerularfiltration. The condition, known as endotheliosis, has been regarded aspathognomonic of preeclampsia. The features have more recently beenidentified in some pregnant women who do not meet the criteria for aclinical diagnosis preeclampsia suggesting that the disorder may affecta larger fraction of pregnant women than is currently recognized.

The pathogenesis of late pregnancy maternal endothelial dysfunction hasbeen the focus of intense study. A growing consensus amongstinvestigators supports the centrality of endothelial dysfunction as theprimary event preceding the development of atherosclerosis. Theendothelium is known to be formed and maintained through stimulation byvarious proangiogenic factors that include vascular endothelial growthfactor (VEGF). The glomerular endothelium is maintained by VEGF releasedby podocytes, specialized epithelial cells juxtaposed to the endotheliumseparated by a permeable basement membrane. Alterations in placentalantiangiogenic factors such as soluble Fms-related tyrosine kinase 1(sFlt-1) and s-Eng (soluble endoglen, coreceptors of TGF-β1) are knownto produce systemic endothelial dysfunction and other manifestations ofpreeclampsia.

While the clinical disease is generally manifested during the lasttrimester by the mother, the pathogenesis of the disease evolves duringthe first trimester. The pathogenesis involves inadequate invasion byextravillous trophoblast into maternal decidual tissues and inadequatetransformation of maternal spiral arteries into high capacitance, lowresistance vessels that are non-responsive to vasoactive agents. Thecause of inadequate invasion has been the focus of research into theetiology of the disease.

Pregnancy in mammals utilizing the hemochorial form of placentationcreates an intimate relationship between genetically different beings,one mature (the mother) and one immature (the fetus and placenta).Extravillous trophoblasts break away from the anchoring placental villiand invade the maternal decidual tissues on their journey to thevessels. They must express appropriate adhesion receptors as well asproteolytic enzymes in a directional manner and be responsive to cueswithin maternal tissues permitting them to attain the appropriate levelof invasion.

Research has identified excess release of a soluble form of receptorsfor angiogenic factors that include sFlt-1 and sEng from thesyncytiotrophoblast as useful in diagnosis and assessment of severity ofpreeclampsia. Increased sFlt-1 identified in maternal peripheral blood,they suggest, corresponds to the degree of third trimester placentalischemia wherein the increased sFlt-1 results in VEGF sequestration andconsequent endothelial dysfunction. Oxygen levels within first trimesterdirectly affect trophoblast invasion. Hypoxia inducible factor 1α(HIF-1α), a transcription factor expressed in cytotrophoblastexperiencing low oxygen conditions, targets Flt-1, VEGFR-2, Tie-1 andTie-2. Another HIF-1α target, TGF-β3, has been shown to blockcytotrophoblast invasion. Hypoxia has been shown to upregulate sFlt-1secretion in primary trophoblast cultures. Based in part on suchobservations, it has be suggested that adequate cytotrophoblast invasionis critical to successful pregnancy. Hypoxic conditions are associatedwith enhanced trophoblast proliferation while normoxic conditions areassociated with enhanced trophoblast invasion. Shortening of the periodof physiologic hypoxia, therefore, would be expected to alter thebalance between proliferation and migration resulting in profoundeffects on the process of spiral artery modification. It has furtherbeen suggested that alterations in angiogenic pathways in earlypregnancy may contribute to inadequate trophoblast invasion of thedecidua and transformation of spiral arteries. A continuous cycleinvolving a deranged balance of angiogenic factors has been postulatedto lead to excess production and release of sFlt-1 into the maternalcirculation.

Local oxygen tension is pivotal in spiral artery transformation. Fromthree weeks EVT first invade spiral arteries resulting in luminalplugging by endovascular resident trophoblast. During a period lastingfrom week four to week eleven of pregnancy, only maternal plasma flowsthrough the placental intervillous space. A state of relative hypoxia isthus maintained. Hydroxylation of HIF1α under normoxic conditionspermits recognition by the von Hippel-Lindau gene product. In cellsexposed to normoxic conditions, HIF-1α is rapidly depleted followingubiquitylation mediated by the von Hippel-Lindau gene product directingits proteasomal degradation. Under hypoxic conditions, HIF-1α isstabilized heterodimerizing with its constitutively expressed partnerHIF-1β migrating to the nucleus where they act as a transcriptionalregulator of numerous genes responsive to hypoxic conditions. Becausehypoxic conditions are associated with enhanced trophoblastproliferation while normoxic conditions are associated with enhancedtrophoblast invasion, shortening of the period of physiologic hypoxia,therefore, would be expected to alter the balance between proliferationand migration. This would result in profound effects on the process ofspiral artery modification.

Attempts to identify markers of trophoblast derangement during theperiod of placenta formation have been made. A variety of proteins andnucleic acid markers released from placental tissues have been founduseful in diagnosis prior to the development of symptoms as early as theend of the first trimester. However, detection of placenta-derivedmarkers during the first trimester is problematic. Several mechanismsmay singly or in aggregate account for poor detection. First, microRNAin plasma or serum is present at low levels requiring their extractionfrom relatively large quantities of plasma or serum. Second, placentalblood flow is blocked during large portions of the first trimesterimposing limited distribution of placental microRNAs into plasma. Third,the placenta is quite small during the first trimester limiting thetotal amount of microRNA produced.

Research has identified a limited repertoire of differentially expressedmicroRNAs in endometrial stromal and glandular epithelial cells isolatedfrom non-pregnant, secretory phase endometrium suggesting the importanceof the endometrial microenvironment on regulation of miRNA expression.Maternal cells are present at the placental site comprisinglocally-fixed cells comprised of epithelio-stromal cells and leukocytesof maternal origin that do not traffic outside of the placental site.The fetus-derived cells and tissues at the placental site constitute afirst foreign, non-self, compartment. The decidual component at theplacental site comprises a second compartment that is chimeric comprisedof both fetal and maternal tissues forming a unique tissue thatexperiences many of the conditions common to the first compartment.These conditions may include autocrine and paracrine influences and,moreover, involve a signaling dialogue between the first and secondcompartments. Thus it might be expected that maternal cells within thesecond compartment might provide significant information uponquantification of microRNAs. However, no suitable and safe methods forcollection of material from first trimester decidua are presentlyavailable.

Therefore, there is a need for characterizing preeclampsia-relatedconditions at early stages, such as in the first trimester or even priorto pregnancy. Further, there is a need for obtaining diagnosticinformation using biological samples that may be collected readily. Thisinvention satisfies these and other needs.

SUMMARY OF THE INVENTION

In accordance with the above needs and those that will be mentioned andwill become apparent below, this disclosure is directed to a method forcharacterizing a preeclampsia-related condition in a subject comprisingcollecting a first non-placental biological sample, extractingmicroRNA-comprising RNA from the first non-placental biological sample,quantifying at least one microRNA within the extracted RNA, andcomparing the quantification of the at least one microRNA to aquantification of the at least one microRNA from a second, non-placentalbiological sample. In one aspect, at least one of the firstnon-placental biological sample and the second non-placental biologicalsample may be taken during a first trimester of pregnancy. In anotheraspect, at least one of the first non-placental biological sample andthe second non-placental biological sample is taken prior to pregnancy.

The first non-placental biological sample and the second non-placentalbiological sample may bridge a therapeutic treatment or may bridge adesignated period of time. In another aspect, the second non-placentalbiological sample may be a control sample.

A suitable method may also include identifying existence of thepreeclampsia-related condition based, at least in part, on thecomparison. A suitable method may also include administering a treatmentbased, at least in part, on the comparison.

In a further aspect, the at least one microRNA may be selected from thegroup consisting of hsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267,hsa-miR-132, hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a,hsa-miR-148a-3p, hsa-miR-155, hsa-miR-16, hsa-miR-181a, hsa-miR-193a-3p,hsa-miR-196a, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210,hsa-miR-219-5p, hsa-miR-221-5p, hsa-miR-223, hsa-miR-301a-3p,hsa-miR-30e-3p, hsa-miR-33a-5p, hsa-miR-340-5p, hsa-miR-424-5p,hsa-miR-513a-5p, hsa-miR-hsa-miR-575, hsa-miR-582-5p, hsa-miR-671-3p andhsa-miR-7-5p. The method may also include quantifying and comparing atleast five microRNAs from the first non-placental biological sample andthe second non-placental biological sample.

In additional aspects, the at least one microRNA bay be selected fromthe group consisting of hsa-miR-223, hsa-miR-7-5p, hsa-miR-148a-3p,hsa-miR-144-3p, hsa-miR-16 and hsa-miR-582-5p, may be selected from thegroup consisting of hsa-miR-144-3p, hsa-miR-148a-3p, hsa-miR-582-5p,hsa-miR-301a-3p, hsa-miR-146a, hsa-miR-575, hsa-miR-199a-5p,hsa-miR-133b and hsa-miR-424-5p, may be selected from the groupconsisting of hsa-miR-210, hsa-miR-1229, hsa-miR-223, hsa-miR-575 andhsa-miR-340-5p, may be selected from the group consisting ofhsa-miR-1229, hsa-miR-146a, hsa-miR-210, hsa-miR-1244, hsa-miR-132 andhsa-miR-133b, may be selected from the group consisting ofhsa-miR-513a-5p, hsa-miR-193a-3p, hsa-miR-7-5p, hsa-miR-575,hsa-miR-221-5p, hsa-miR-133b, hsa-miR-1 and hsa-miR-199a-5, may beselected from the group consisting of hsa-miR-513a-5p, hsa-miR-193a-3p,hsa-miR-221-5p, hsa-miR-340-5p, hsa-miR-7-5p, hsa-miR-575, hsa-miR-1,hsa-miR-199a-5p and hsa-miR-33a-5p, may be selected from the groupconsisting of hsa-miR-513a-5p, hsa-miR-193a-3p, hsa-miR-7-5p,hsa-miR-575, hsa-miR-221-5p, hsa-miR-133b, hsa-miR-1 andhsa-miR-199a-5p, may be selected from the group consisting ofhsa-miR-575, hsa-miR-144-3p, hsa-miR-148a-3p, hsa-miR-210,hsa-miR-193a-3p, hsa-miR-199b-5p and hsa-miR-199a-5p, may be selectedfrom the group consisting of miR575, miR144-3p, miR199a-5p, miR210,miR1229, miR133b, miR148a-3p, miR193a-3p, miR7-5p, miR223 and miR340-5p,may be selected from the group consisting of hsa-miR-144-3p,hsa-miR-582-5p, hsa-miR-30e-3p, hsa-miR-340-5p, hsa-miR-424-5p,hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210, hsa-miR-221-5p,hsa-miR-33a-5p, hsa-miR-575, hsa-miR-7-5p, hsa-miR-1229, hsa-miR-1267and hsa-miR-671-3p, or may be selected from the group consisting ofhsa-miR-1181, hsa-miR-1296, hsa-miR-132, hsa-miR-136, hsa-miR-141,hsa-miR-142-5p, hsa-miR-144, hsa-miR-153, hsa-miR-1537, hsa-miR-193a-3p,hsa-miR-196a, hsa-miR-219-5p, hsa-miR-29b, hsa-miR-301a, hsa-miR-32,hsa-miR-33a, hsa-miR-545, hsa-miR-582-3p and hsa-miR-590-5p.

This disclosure also includes a diagnostic kit for characterizing apreeclampsia-related condition, such as a kit comprising a microarrayincluding at least five microRNAs selected from the group consisting ofhsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267, hsa-miR-132,hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a, hsa-miR-148a-3p,hsa-miR-155, hsa-miR-16, hsa-miR-181a, hsa-miR-193a-3p, hsa-miR-196a,hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210, hsa-miR-219-5p,hsa-miR-221-5p, hsa-miR-223, hsa-miR-301a-3p, hsa-miR-30e-3p,hsa-miR-33a-5p, hsa-miR-340-5p, hsa-miR-424-5p, hsa-miR-513a-5p,hsa-miR-hsa-miR-575, hsa-miR-582-5p, hsa-miR-671-3p and hsa-miR-7-5p

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages will become apparent from the followingand more particular description of the preferred embodiments of theinvention, as illustrated in the accompanying drawings, and in whichlike referenced characters generally refer to the same parts or elementsthroughout the views, and in which:

FIG. 1 lists 30 microRNAs demonstrating potential for predictingpregnancy outcome based on pregnancy outcome data, according to anembodiment;

FIG. 2 shows differences in preconception microRNA levels betweenHealthy and Preeclampsia patients of the microRNAs of FIG. 1;

FIG. 3 shows differences in preconception microRNA levels between MostHigh Risk Outcome Group and Lowest Risk Outcome Group, according to anembodiment;

FIG. 4 shows results associated with first trimester pregnancy testing,according to an embodiment;

FIG. 5 shows differences between sequential microRNA levels in earlypregnancy between Preeclampsia and Healthy pregnancies, according to anembodiment;

FIG. 6 shows differences between preeclampsia and healthy microRNA levelchanges, according to an embodiment;

FIG. 7 shows differences in microRNA response between preeclampsia andhealthy pregnancies with and without IVIG, according to an embodiment;

FIG. 8 correlates selected microRNAs associated with poor pregnancyoutcomes with IVIG response, according to an embodiment;

FIG. 9 shows the Effect of Lymphocyte Immunotherapy, according to anembodiment;

FIG. 10 shows an exemplary scoring system as applied to selectedmircoRNAs, according to an embodiment;

FIG. 11 shows microRNA significance as scored based on the frequency ofthe presence of the microRNA in the above figures;

FIGS. 12a and 12b show a microRNA pregnancy outcome predictor scoringsystem, according to an embodiment;

FIG. 13 shows exemplary ROC curve calculations for analysis of themicroRNA preeclampsia scoring system, according to an embodiment;

FIG. 14 shows ROC curve analyses of FIG. 13;

FIGS. 15a and 15b show IVIG response data, according to an embodiment;

FIGS. 16a and 16b show pregnancy outcome data, according to anembodiment;

FIG. 17 shows 15 top microRNA marker candidates obtained from microarrayanalysis of 962 microRNAs, according to an embodiment;

FIG. 18 shows an exemplary microRNA pregnancy outcome predictor,according to an embodiment;

FIGS. 19a and 19b show microRNA IVIG response data combined withmicroRNA pregnancy outcome data, according to an embodiment;

FIGS. 20a and 20b show selected microRNAs having significant pregnancyoutcome prediction, according to an embodiment;

FIG. 21 shows the top 25 differences between mean Healthy level and meanPreeclampsia and miscarriage levels before IVIG, according to anembodiment; and

FIG. 22 shows the bottom 25 differences between mean Healthy level andmean Preeclampsia and miscarriage levels before IVIG, according to anembodiment.

DETAILED DESCRIPTION OF THE INVENTION

At the outset, it is to be understood that this disclosure is notlimited to particularly exemplified materials, architectures, routines,methods or structures as such may, of course, vary. Thus, although anumber of such option, similar or equivalent to those described herein,can be used in the practice of embodiments of this disclosure, thepreferred materials and methods are described herein.

It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments of this disclosure only andis not intended to be limiting.

This summary provides a listing of several embodiments of the presentlydisclosed subject matter. However, it should be understood thatvariations and permutations of these embodiments exist. This summary isintended to serve as exemplary of potential embodiments. Unlessotherwise indicated, all numbers expressing quantities of ingredients,reaction conditions, and so forth used in the specification and claimsare to be understood as being modified in all instances by the term“about”. Accordingly, unless indicated to the contrary, the numericalparameters set forth in this specification are approximations that canvary depending upon the desired properties sought to be obtained by thepresently disclosed subject matter.

Herein, specific microRNAs may be identified by their prefix mir- andtheir identifier, such as mir-155. Sequences within an RNA transcripttargeted by miRNAs may lie anywhere within the transcript. However,sequences within the 3′ untranslated region are most common. MicroRNAnomenclature comprises a three-letter prefix “mir” followed by a numberassigned generally in order of the description of the microRNA. In oneconvention when the “R” is lower case, the sequence refers to thepre-microRNA while when upper case is employed (miR), the mature form isindicated. Variants where the sequences vary by one or two bases may bedesignated by the letters “a” and “b”. Occasionally, pre-microRNAslocated within separate regions of the genome result in an identicalmature microRNA. These microRNAs are distinguished by a numeric suffix(“miR-123-1” and “miR-123-2”). When two microRNAs originate fromopposite arms of the same pre-microRNA they are designated with thesuffix -3p or -5p according to whether the 3′ or 5′ strand is used. Asused herein, the numeric code, e.g. “mir-123” shall include its variantssuch as mir-123-1, mir123-2, and the -3p and -5p variants. As usedherein the term “pri-miRNA” shall mean the RNA targeted by theDrosha-Pasha complex. As used herein the term “pre-miRNA” shall mean theproduct of the cleavage by the Drosha-Pasha complex. As used herein, nodistinction shall be made between sequences between the parentnomenclature for example mir-123 and any more selective sequence forexample mir-123-5p and other than by description within the text.

As will be discussed below, examples of suitable microRNAs that may beused according to this disclosure include, without limitation,hsa-miR-582-3p MIMAT0004797 (SEQ ID NO: 1); hsa-miR-7-1-3p MIMAT0004553(SEQ ID NO: 2); hsa-miR-340-5p MIMAT0004692 (SEQ ID NO: 3);hsa-miR-199b-3p MIMAT0004563 (SEQ ID NO: 4); hsa-miR-199a-3pMIMAT0000232 (SEQ ID NO: 5); hsa-miR-30e-5p MIMAT0000692 (SEQ ID NO: 6);hsa-miR-575 MIMAT0003240 (SEQ ID NO: 7); hsa-miR-7-5p MIMAT0000252 (SEQID NO: 8); hsa-miR-33a-3p MIMAT0004506 (SEQ ID NO: 9); hsa-miR-7-2-3pMIMAT0004554 (SEQ ID NO: 10); hsa-miR-199b-5p MIMAT0000263 (SEQ ID NO:11); hsa-miR-144-5p MIMAT0004600 (SEQ ID NO: 12); hsa-miR-30e-3pMIMAT0000693 (SEQ ID NO: 13); hsa-miR-424-3p MIMAT0004749 (SEQ ID NO:14); hsa-miR-33a-5p MIMAT0000091 (SEQ ID NO: 15); hsa-miR-671-3pMIMAT0004819 (SEQ ID NO: 16); hsa-miR-340-3p MIMAT0000750 (SEQ ID NO:17); hsa-miR-1267 MIMAT0005921 (SEQ ID NO: 18); hsa-miR-1229-3pMIMAT0005584 (SEQ ID NO: 19); hsa-miR-424-5p MIMAT0001341 (SEQ ID NO:20); hsa-miR-221-3p MIMAT0000278 (SEQ ID NO: 21); hsa-miR-1 MIMAT0000416(SEQ ID NO: 22); hsa-miR-133b MIMAT0000770 (SEQ ID NO: 23);hsa-miR-221-5p MIMAT0004568 (SEQ ID NO: 24); hsa-miR-210 MIMAT0000267(SEQ ID NO: 25); hsa-miR-1229-5p MIMAT0022942 (SEQ ID NO: 26);hsa-miR-671-5p MIMAT0003880 (SEQ ID NO: 27); hsa-miR-582-5p MIMAT0003247(SEQ ID NO: 28); hsa-miR-199a-5p MIMAT0000231 (SEQ ID NO: 29);hsa-miR-144-3p MIMAT0000436 (SEQ ID NO: 30); hsa-miR-376a-5pMIMAT0003386 (SEQ ID NO: 31); hsa-miR-193a-3p MIMAT0000459 (SEQ ID NO:32); hsa-miR-557 MIMAT0003221 (SEQ ID NO: 33); hsa-miR-34a-3pMIMAT0004557 (SEQ ID NO: 34); hsa-miR-584-5p MIMAT0003249 (SEQ ID NO:35); hsa-miR-1244 MIMAT0005896 (SEQ ID NO: 36); hsa-miR-125b-1-3pMIMAT0004592 (SEQ ID NO: 37); hsa-miR-32-3p MIMAT0004505 (SEQ ID NO:38); hsa-miR-933 MIMAT0004976 (SEQ ID NO: 39); hsa-miR-373-5pMIMAT0000725 (SEQ ID NO: 40); hsa-let-7b-5p MIMAT0000063 (SEQ ID NO:41); hsa-miR-376a-3p MIMAT0000729 (SEQ ID NO: 42); hsa-miR-129-2-3pMIMAT0004605 (SEQ ID NO: 43); hsa-miR-548am-3p MIMAT0019076 (SEQ ID NO:44); hsa-let-7f-5p MIMAT0000067 (SEQ ID NO: 45); hsa-miR-876-3pMIMAT0004925 (SEQ ID NO: 46); hsa-miR-371a-5p MIMAT0004687 (SEQ ID NO:47); hsa-miR-423-5p MIMAT0004748 (SEQ ID NO: 48); hsa-miR-373-3pMIMAT0000726 (SEQ ID NO: 49); hsa-miR-152 MIMAT0000438 (SEQ ID NO: 50);hsa-miR-34a-5p MIMAT0000255 (SEQ ID NO: 51); hsa-miR-335-5p MIMAT0000765(SEQ ID NO: 52); hsa-miR-181c-5p MIMAT0000258 (SEQ ID NO: 53);hsa-miR-125b-2-3p MIMAT0004603 (SEQ ID NO: 54); hsa-miR-548am-5pMIMAT0022740 (SEQ ID NO: 55); hsa-miR-338-3p MIMAT0000763 (SEQ ID NO:56); hsa-miR-1225-5p MIMAT0005572 (SEQ ID NO: 57); hsa-miR-362-3pMIMAT0004683 (SEQ ID NO: 58); hsa-miR-767-5p MIMAT0003882 (SEQ ID NO:59); hsa-miR-136-3p MIMAT0004606 (SEQ ID NO: 60); hsa-miR-29b-1-5pMIMAT0004514 (SEQ ID NO: 61); hsa-miR-29a-3p MIMAT0000086 (SEQ ID NO:62); hsa-miR-92b-3p MIMAT0003218 (SEQ ID NO: 63); hsa-miR-362-5pMIMAT0000705 (SEQ ID NO: 64); hsa-miR-223-5p MIMAT0004570 (SEQ ID NO:65); hsa-miR-505-3p MIMAT0002876 (SEQ ID NO: 66); hsa-miR-634MIMAT0003304 (SEQ ID NO: 67); hsa-miR-371a-3p MIMAT0000723 (SEQ ID NO:68); hsa-miR-129-1-3p MIMAT0004548 (SEQ ID NO: 69); hsa-miR-1238-5pMIMAT0022947 (SEQ ID NO: 70); hsa-miR-876-5p MIMAT0004924 (SEQ ID NO:71); hsa-miR-181c-3p MIMAT0004559 (SEQ ID NO: 72); hsa-miR-338-5pMIMAT0004701 (SEQ ID NO: 73); hsa-miR-505-5p MIMAT0004776 (SEQ ID NO:74); hsa-miR-335-3p MIMAT0004703 (SEQ ID NO: 75); hsa-miR-543MIMAT0004954 (SEQ ID NO: 76); hsa-miR-223-3p MIMAT0000280 (SEQ ID NO:77); hsa-miR-125b-5p MIMAT0000423 (SEQ ID NO: 78); hsa-miR-1238-3pMIMAT0005593 (SEQ ID NO: 79); hsa-miR-377-5p MIMAT0004689 (SEQ ID NO:80); hsa-miR-584-3p MIMAT0022708 (SEQ ID NO: 81); hsa-miR-22-5pMIMAT0004495 (SEQ ID NO: 82); hsa-miR-376a-2-5p MIMAT0022928 (SEQ ID NO:83); hsa-miR-301a-5p MIMAT0022696 (SEQ ID NO: 84); hsa-miR-548mMIMAT0005917 (SEQ ID NO: 85); hsa-miR-29b-3p MIMAT0000100 (SEQ ID NO:86); hsa-miR-99a-3p MIMAT0004511 (SEQ ID NO: 87); hsa-miR-33b-3pMIMAT0004811 (SEQ ID NO: 88); hsa-miR-92b-5p MIMAT0004792 (SEQ ID NO:89); hsa-miR-602 MIMAT0003270 (SEQ ID NO: 90); hsa-miR-1237-3pMIMAT0005592 (SEQ ID NO: 91); hsa-miR-129-5p MIMAT0000242 (SEQ ID NO:92); hsa-miR-148b-3p MIMAT0000759 (SEQ ID NO: 93); hsa-miR-377-3pMIMAT0000730 (SEQ ID NO: 94); hsa-let-7b-3p MIMAT0004482 (SEQ ID NO:95); hsa-miR-125a-5p MIMAT0000443 (SEQ ID NO: 96); hsa-miR-125a-3pMIMAT0004602 (SEQ ID NO: 97); hsa-miR-148b-5p MIMAT0004699 (SEQ ID NO:98); hsa-miR-22-3p MIMAT0000077 (SEQ ID NO: 99); hsa-miR-1237-5pMIMAT0022946 (SEQ ID NO: 100); hsa-let-7f-1-3p MIMAT0004486 (SEQ ID NO:101); hsa-miR-29a-5p MIMAT0004503 (SEQ ID NO: 102); hsa-miR-193a-5pMIMAT0004614 (SEQ ID NO: 103); hsa-miR-423-3p MIMAT0001340 (SEQ ID NO:104); hsa-miR-191-3p MIMAT0001618 (SEQ ID NO: 105); hsa-miR-301a-3pMIMAT0000688 (SEQ ID NO: 106); hsa-miR-767-3p MIMAT0003883 (SEQ ID NO:107); hsa-miR-563 MIMAT0003227 (SEQ ID NO: 108); hsa-miR-95 MIMAT0000094(SEQ ID NO: 109); hsa-miR-1234-3p MIMAT0005589 (SEQ ID NO: 110);hsa-miR-1225-3p MIMAT0005573 (SEQ ID NO: 111); hsa-miR-136-5pMIMAT0000448 (SEQ ID NO: 112); hsa-miR-1234-5p MIMAT0022944 (SEQ ID NO:113); hsa-miR-99a-5p MIMAT0000097 (SEQ ID NO: 114); hsa-miR-32-5pMIMAT0000090 (SEQ ID NO: 115); hsa-miR-191-5p MIMAT0000440 (SEQ ID NO:116); hsa-miR-33b-5p MIMAT0003301 (SEQ ID NO: 117); hsa-mir-1-1MI0000651 (SEQ ID NO: 118); hsa-mir-1-2 MI0000437 (SEQ ID NO: 119);hsa-mir-7-1 MI0000263 (SEQ ID NO: 120); hsa-mir-7-2 MI0000264 (SEQ IDNO: 121); hsa-mir-7-(SEQ ID NO:3 MI0000265 122); hsa-mir-30e MI0000749(SEQ ID NO: 123); hsa-mir-33a MI0000091 (SEQ ID NO: 124); hsa-mir-133bMI0000822 (SEQ ID NO: 125); hsa-mir-144 MI0000460 (SEQ ID NO: 126);hsa-mir-199a-1 MI0000242 (SEQ ID NO: 127); hsa-mir-199a-2 MI0000281 (SEQID NO: 128); hsa-mir-199b MI0000282 (SEQ ID NO: 129); hsa-mir-210MI0000286 (SEQ ID NO: 130); hsa-mir-221 MI0000298 (SEQ ID NO: 131);hsa-mir-340 MI0000802 (SEQ ID NO: 132); hsa-mir-424 MI0001446 (SEQ IDNO: 133); hsa-mir-575 MI0003582 (SEQ ID NO: 134); hsa-mir-582 MI0003589(SEQ ID NO: 135); hsa-mir-671 MI0003760 (SEQ ID NO: 136); hsa-mir-1229MI0006319 (SEQ ID NO: 137); hsa-mir-1267 MI0006404 (SEQ ID NO: 138);hsa-let-7a-3 MI0000062 (SEQ ID NO: 139); hsa-let-7e MI0000066 (SEQ IDNO: 140); hsa-mir-22 MI0000078 (SEQ ID NO: 141); hsa-mir-29a MI0000087(SEQ ID NO: 142); hsa-mir-29b-1 MI0000105 (SEQ ID NO: 143); hsa-mir-32MI0000090 (SEQ ID NO: 144); hsa-mir-33b MI0003646 (SEQ ID NO: 145);hsa-mir-34a MI0000268 (SEQ ID NO: 146); hsa-mir-92b MI0003560 (SEQ IDNO: 147); hsa-mir-95 MI0000097 (SEQ ID NO: 148); hsa-mir-99a MI0000101(SEQ ID NO: 149); hsa-mir-125a MI0000469 (SEQ ID NO: 150);hsa-mir-125b-1 MI0000446 (SEQ ID NO: 151); hsa-mir-125b-2 MI0000470 (SEQID NO: 152); hsa-mir-129-1 MI0000252 (SEQ ID NO: 153); hsa-mir-129-2MI0000473 (SEQ ID NO: 154); hsa-mir-136 MI0000475 (SEQ ID NO: 155);hsa-mir-148b MI0000811 (SEQ ID NO: 156); hsa-mir-152 MI0000462 (SEQ IDNO: 157); hsa-mir-181c MI0000271 (SEQ ID NO: 158); hsa-mir-191 MI0000465(SEQ ID NO: 159); hsa-mir-193a MI0000487 (SEQ ID NO: 160); hsa-mir-223MI0000300 (SEQ ID NO: 161); hsa-mir-301a MI0000745 (SEQ ID NO: 162);hsa-mir-335 MI0000816 (SEQ ID NO: 163); hsa-mir-338 MI0000814 (SEQ IDNO: 164); hsa-mir-362 MI0000762 (SEQ ID NO: 165); hsa-mir-371a MI0000779(SEQ ID NO: 166); hsa-mir-373 MI0000781 (SEQ ID NO: 167); hsa-mir-376a-1MI0000784 (SEQ ID NO: 168); hsa-mir-376a-2 MI0003529 (SEQ ID NO: 169);hsa-mir-377 MI0000785 (SEQ ID NO: 170); hsa-mir-423 MI0001445 (SEQ IDNO: 171); hsa-mir-425 MI0001448 (SEQ ID NO: 172); hsa-mir-505 MI0003190(SEQ ID NO: 173); hsa-mir-543 MI0005565 (SEQ ID NO: 174); hsa-mir-548mMI0006400 (SEQ ID NO: 175); hsa-mir-557 MI0003563 (SEQ ID NO: 176);hsa-mir-563 MI0003569 (SEQ ID NO: 177); hsa-mir-584 MI0003591 (SEQ IDNO: 178); hsa-mir-602 MI0003615 (SEQ ID NO: 179); hsa-mir-634 MI0003649(SEQ ID NO: 180); hsa-mir-767 MI0003763 (SEQ ID NO: 181); hsa-mir-876MI0005542 (SEQ ID NO: 182); hsa-mir-933 MI0005755 (SEQ ID NO: 183);hsa-mir-1225 MI0006311 (SEQ ID NO: 184); hsa-mir-1234 MI0006324 (SEQ IDNO: 185); hsa-mir-1237 MI0006327 (SEQ ID NO: 186); hsa-mir-1238MI0006328 (SEQ ID NO: 187); hsa-mir-1244-1 MI0006379 (SEQ ID NO: 188);hsa-mir-1244-2 MI0015974 (SEQ ID NO: 189); hsa-mir-1244-3 MI0015975 (SEQID NO: 190); and hsa-mir-1825 MI0008193 (SEQ ID NO: 191)

As used herein the term “placental site” shall refer to the discretearea of the maternal endometrium in direct contact with the implantingfeto-placental unit. It is coextensive with the placenta. Duringpregnancy the stromal elements undergo decidual transformation whereinthe elongated fibroblast like cells of the stroma are transformed intoplump secretory-like cells.

Specific microRNA abbreviations may also include an additional prefixidentifying the species of origin, such as hsa for homo sapiens.Although the primary embodiments described herein are directed tohumans, one of skill in the art will appreciate that the techniques ofthis disclosure can be applied to other species.

Preeclampsia-related condition: As used herein the term“Preeclampsia-related condition” shall comprise one or more conditionsselected from a group exemplified by but is not limited to preeclampsiaand conditions associated with preeclampsia or related to preeclampsiaby related etiology or symptoms including premature rupture of membranes(PROM), intrauterine growth retardation (IUGR), gestational diabetes,proteinuria, hypertension, edema, HELLP Syndrome, eclampsia, low birthweight, miscarriage, pregnancy-induced hypertension, Metabolic Syndromeassociated with heart disease, pregnancy bleeding, placental abruption,placenta accreta, placental hemorrhage, placental infarction, pretermlabor, preterm birth, stillbirth, excess or low amniotic fluid,subchorionic hemorrhage, recurrent pregnancy loss and anti-phospholipidantibody syndrome, thrombophilia, fetal thrombotic vasculopathy,villitis of unknown etiology, poor endometrial lining development,infertility and infertility in humans or other mammals. These conditionsare correlated with changes preceding and following pregnancy and areunderstood to be within the scope of the invention.

The applicants have identified metabolic and signaling pathways thatappear to be common to the conditions listed within the definition.Moreover, it is well understood in the art that these conditions sharein common etiopathologic features and therefore may share commonalterations in expression of microRNAs and microRNA profiles.

The term “control individual” as used herein has a special meaning. A“control individual” shall mean individuals of comparablecharacteristics such as age and sex who do not have preeclampsia andrelated conditions or pathology leading to said condition and are not atknown risk of developing said condition. The term “control sample” asused herein shall mean a non-placental biological sample from the samesource, such a peripheral blood, and collected under the same orcomparable conditions as a patient sample comprising cells of thenon-placental biological sample collected from a control individual thatis processed and analyzed in the same manner as a patient sample.

It is further understood that the term “control sample” as used hereinmay represent the mathematical mean of multiple samples from controlindividuals wherein a number of samples considered sufficient by anindividual of ordinary skill in the art are collected. Additionalstatistical parameters may be derived from said samples such as standarddeviation of the mean. Said additional statistical parameters may beused for purposes of comparison of a patient test result with controlsamples to estimate the probability that the patient's test resultrepresents an abnormal finding and, thereby suggests that the patient issuffering from preeclampsia and related conditions or risk of saidcondition. For purposes of simplicity the term may also be used inanother way wherein a plurality of comparable, temporally separate,samples are collected and assayed from a single individual and comparedwith one another such that a first sample or a particular subsequentsample are compared as though the first is a control for the second,permitting assessment of a change in condition potentially as a functionof the clinical state, or stage of pregnancy or as a result oftherapeutic intervention.

Small non-coding RNA: As used herein the term “small, non-coding RNA”shall mean a polynucleotide ranging from about 18-31 RNA nucleotides notcoding for a polypeptide. Small, non-coding RNAs are single-stranded RNAmolecules of about 18-31 nucleotides in length, which regulate geneexpression. Small non-coding RNAs are encoded by genes from whose DNAthey are transcribed in a similar manner to mRNA that encode protein.Small non-coding RNA do not encode protein. Three forms have beendescribed. Piwi-RNA comprise up to 31 nucleotides while si-RNA and miRNAcomprise approximately 18-25 nucleotides. The genes are processed fromRNA transcripts that are much longer than the mature non-coding RNA.miRNAs are initially transcribed as a transcript known as pri-miRNAcomprising a cap and poly-A tail. It is subsequently processed into amuch shorter transcript within the nucleus by a protein complex known asthe Microprocessor complex. The complex containing an RNase III known asDrosha and an a double stranded RNA recognizing molecule known as Pasharecognize a stem-loop structure and cleave the pri-miRNA into astem-loop structure of approximately 70 nucleotides known as pre-miRNA.Pre-miRNAs are escorted to the cytoplasm with the aid of Transportin-5.These pre-miRNAs are then processed into the mature miRNA in thecytoplasm by a second RNase III, Dicer initiating formation of a proteincomplex known as the RNA-induced silencing complex (RISC).Dicer-mediated cleavage generates two separate RNA semi-complementarystrands, one of which is selected for integration into the RISC complex.The integrated strand, known as the guide strand, forms a complementaryinteraction with a target mRNA formed, most commonly, along a short 6-8base region of the guide strand. The RISC complex generally aligns alonga region of the 3′ untranslated portion of the mRNA. Argonaute proteinswithin the RISC complex then act to cleave (degrade) or suppresstranslation of the mRNA polynucleotide generally depending upon thedegree of miRNA complementarity. Nomenclature for miRNAs as used hereinmay be found in miRBase (www.mirbase.org). The entries represent thepredicted hairpin portion of the miRNA transcript.

MicroRNA sequences are most commonly found within the introns of theirhost genes. They may also be found within exons and across exon-intronboundaries. These sequences are known to target at least 30 percent ofall human genes, fine-tuning their expression. The final short sequenceis generated through a series of cleavages involving two enzymes, Droshaand Dicer, from relatively long RNA primary RNA sequences.

miRNA Profiles: As used herein the term “miRNA Profile” shall mean agroup comprising one or more microRNAs that can be used to distinguishtwo non-placental biological samples. Expression of miRNAs within anybiologic sample is known to be quite variable though each microRNA,individually, might correlate with a clinical finding. To improveclinical correlation a plurality of microRNAs, each correlating with aclinical condition are viewed in aggregate. A comparison of theirquantitative expression between any two clinical conditions, preferablywhere one is a normal or control, may be used to improve distinctionbetween two clinical conditions. A variety of methods may be used tocompare the patterns expressed between two clinical conditions such asproviding individual numerical scores that can be compared, for example,as a sum.

As used herein, the term “about,” when referring to a value or to anamount of mass, weight, time, volume, concentration or percentage ismeant to encompass variations of in some embodiments ±20%, in someembodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, insome embodiments ±0.5%, and in some embodiments ±0.1% from the specifiedamount, as such variations are appropriate to perform the disclosedmethods.

As used herein the term “heuristic” shall refer to experience-basedtechniques for problem solving. More specifically, it shall comprisetechniques designed for solving problems based on experience such asthose comprised in a database. Moreover, the techniques may involve aprocess of continual refinement wherein a problem-solving model iscontinually updated based upon accrual of additional data into thedatabase. These techniques may be incorporated into computer algorithms.

Non-Placental Biological Sample: As used herein, the term “non-placentalbiological sample” shall mean maternal cells and derivatives thereof notcollected from the placental site. Suitable techniques include isopycnicdensity-gradient centrifugation as well as monoclonal antibodyparamagnetic bead conjugates for example used in techniques well knownin the art. Isolated cells may be interrogated in batch assays assessingthe total quantity of a specific microRNA that may be related to theaverage quantity expressed by cells of the individual cell type or maybequantified by in situ hybridization. Advantage may be taken of therelative quantity of cell-comprised microRNA versus the quantity ofmicroRNA comprised in the blood liquid phase as in plasma orserum-comprised vesicular structures. The relative quantity of microRNAin the former is very substantially greater than the later permittingassessment of cellular microRNA as a measured by total blood microRNA.The PAXgene blood RNA Tube™ is designed for the collection, storage,stabilization and transport of intracellular RNA. They may be used inconjunction with a nucleic acid purification kit (PAXgene Blood RNA Kit)for isolation of cellular microRNA.

A non-placental biological sample may be derived from a patient with adisease or being investigated for the propensity or likelihood ofdeveloping a disease or following pregnancy for a period of about sixmonths. As used herein, the term “subject” refers to any mammal,including both human and other mammals. Preferably, the methods of thepresent invention are applied to human subjects.

All patents and references whether conventionally cited in theliterature or addressed through internet links herein are incorporatedin entirety by reference. All technical and scientific terms used withinthis description shall have the same meaning as commonly understood bythose of ordinary skill in the art disclosed herein except whereotherwise specifically defined. Following long-standing patent lawconvention, the terms “a”, “an”, and “the” refer to “one or more” whenused in this application, including the claims. Thus, for example,reference to “a peptide” includes a plurality of such peptides, and soforth.

As referenced above, an unmet need remains to characterize apreeclampsia-related condition using a biological sample that may bereadily collected. The techniques of this invention may include maternalcells available for interrogation that constitute a third compartment,the maternal cellular compartment not collected from the placental site.Conventionally, cells of this third compartment may not be expected toexperience meaningful exposure to conditions experienced by the firstand second compartments. In the mature placenta, maternal blood entersthe uterus by arcuate, radial and spiral arteries before entering theintervillous space being drained by uterine veins and thence reenteringmaternal circulation. The intervillous space acts as a shunt during thelater portions of pregnancy with only minimal exposure of maternal bloodcells to placental conditions unlike non-migratory cells stationedwithin the second compartment. Because of trophoblast plugging duringthe first trimester, maternal blood, at least its cellular components,are redirected through myometrial shunt pathways, thereby avoidingexposure to the microenvironment of the placental site. Because thesecells do not experience the local environment of the placental site, theprior art provides no indication that they may be expected to reflect adifferentially expressed microRNA profile.

According to the techniques of this disclosure, individual microRNAs andmicroRNA profiles have been found to demonstrate differential expressionpatterns between patients with preeclampsia and related conditions orrisk of developing said disorder and those with uneventful pregnancies.These differentially expressed microRNAs within maternal cells notcollected from the placental site during pregnancy were analyzed usingthe DIANA-micro-T-CDS (v5.0) (accessed May 12, 2013 athttp://diana.cslab.ece.ntua.gr/), providing unexpected results.Signaling and metabolic pathways regulated in aggregate by thedifferentially-expressed microRNAs included the Glycosaminoglycanbiosynthesis (heparan sulfate pathway (hsa00534)), Mucin type 0-Glycanbiosynthesis pathway (hsa00512), the Wnt signaling pathway (hsa04310),the TGF-beta signaling pathway (hsa04350) and the ECM-receptorinteraction (hsa04512) pathway. The prevailing teachings within theliterature suggest their differential regulation in trophoblast andpossibly maternal cells of the placental site. Conversely, there is noapparent teaching in the prior art of differential expression inmaternal cells outside of the placental site.

Clinicians may be presented by patients in whom an immunotherapy isthought to be useful. Appropriate selection of patients forimmunotherapy may be central to effective therapeutic intervention.Appropriate patients for such intervention may be selected by the use ofvarious PBMC in vitro markers. According to the present disclosure,quantification of various microRNAs and patterns of microRNA change inPBMCs at various time points prior to and following immunotherapeuticintervention may be performed. These microRNA “signatures” support theclinical diagnosis, through identification of candidates for particulartherapeutic intervention(s), and prognosticate outcome in patients withvarious disorders, for example, pregnancy-related disorders. Moreover,it is contemplated that the diagnostic procedures of the presentinvention may be applied to different clinical conditions and differentimmunotherapeutic interventions. Their use simplifies complex diagnosticstrategies into a single procedure and provides information heretoforeunavailable.

While initial studies and examples described herein substantially relateto pregnancy and disorders of the reproductive system of women, thesestudies should be regarded as exemplary of the broader application ofthe present invention to other disease states involving other organsystems. Moreover, while some descriptions relate to changes in theexpression of one or more microRNAs before and after a selectedintervention, it is understood that the present invention is applicableto measurements made at a single time point whether before or after acontemplated intervention.

A novel aspect of the present invention is the separation of patientsinto groups distinguishable by characteristic changes in single ormultiple microRNAs following the selected intervention. Identificationof patients belonging to microRNA response groups is associated withimproved efficacy, prognosis and utility of particular immunotherapeuticintervention(s). Moreover, quantitative levels of certain microRNAs andpatterns of change within microRNAs may predict patient responsegroup(s) and post-therapy levels may have additional predictive value.Use of microRNA patterns responsive to therapeutic intervention orpredictive thereof provides useful insights into management unavailablethrough identification of markers directly related to the pathologicprocess.

The presence, absence or level of the DNA-interacting proteins is theprimary regulator of the effect of such native DNA interactions. Withrespect to purification, it is clear that such interactions may occuronly in the native, unpurified form of the DNA where additionalinteractions with auxiliary-interacting proteins effecting of a resultof such an interaction. The “state” of a cell e.g. proliferation,stressed, differentiation, is not comprised within such sequences butrather the concerted interaction of DNA and the interacting proteins.Separation of DNA by isolation or disruption in situ by denaturingprocesses including heat or chemicals or by the interaction of invasivepolynucleotide probes (locked-DNA, PNAs etc.) disrupts theseinteractions. In terms of RNA, the effects are even more clear. It isnow well established that RNA molecules form non-canonic interactionsresulting in unexpected activities such as found in ribozymes. Hereessential interactions with specific divalent cations are required forassumption of a catalytic conformation. MicroRNAs require interactionwith proteins that must be available in the appropriate format.

The process of discovery involved is a transformative step. Theprocedure used to discover the microRNAs of this invention may involvetwo or more testing points, such as preceding and following anintervention designed to perturb the system or after a given period oftime to provide insight into changes in the course of thepreeclampsia-related condition. MicroRNAs were identified thatdemonstrated markedly different behavior following the perturbingintervention. The intervention of this invention was transformativeresulting in a distinguishable response amongst selected microRNAsbetween different clinical subsets. Moreover, a single testing doneprior to intervention reveals multiple microRNAs, mainly the samemicroRNAs showing distinguishable changes between clinical subgroups maybe used to predict membership amongst clinical subgroups.

Quantification of microRNAs provides insights into physiologic andpathologic processes wherein their levels are measurably distinguishablefrom the “steady-state”. The interactions between genetic andenvironmental factors are understood to result in an expressed phenotypenot predictable by genetic factors alone. In the last few years,interactions at the level of microRNAs and environmental factors havebecome apparent.

Thus it is understood profile in the present invention that a miRNA maybe used to differentiate between a condition 1 and another condition 2.For example, condition 1 may be found in a patient at a given timepoint. Condition 2 may be any of a number of conditions to whichcondition 1 is compared. For example, condition 2 may be derived fromthe corresponding non-placental biological sample at the same time pointduring pregnancy or peri-pregnancy period. It is understood thatcondition 2 may be a composite or average of a plurality of microRNAs.Condition 2 may represent a second time point wherein differencesbetween condition 1 and condition 2 are assessed.

The common miRNA or non-coding RNA signature profile can be calculated“on-the-fly” from a plurality of miRNA-profiles that are stored, e.g. indatabase. As the database increased in size, the predictive value of thesignature profile becomes stronger, with a greater number and accuracyof predictions becoming possible. The common miRNA signature profilewhich is able to differentiate between a condition 1 and anothercondition 2 is changing as soon as an new profile is added to thedatabase which is relevant to either to state of health 1 or anothercondition 2. In this respect it is different from a predetermined set ofmiRNAs (see above). Furthermore, the basis for generating the commonmiRNA signature profile—hence the miRNA profiles stored in thedatabase—is generated from capture probes, e.g. on a matrix that isrepresenting as much as possible different capture probes for detectingas much as possible, ideally all known, miRNAs.

It is understood that data may be collected together with clinicalinformation including the conditions, times within pregnancy orperi-pregnancy period, treatment and symptoms in a database that isdynamic and growing with accumulation of data. Analysis of a microRNAsignature of a non-placental biological sample may be compared withcorresponding microRNA signature derived from the database. Mathematicalapproaches to analysis of data and methods for comparison are well knownto those skilled in the art. These methods include, for example, Signalto Noise ratios, Fold Quotients, correlation and statistical methods ashypothesis tests such as t-test, the Wilcoxon-Mann-Whitney test, theArea under the Receiver operator Characteristics Curve Information.Theory approaches, for example, the Mutual Information, Cross-entropy,Probability theory, for example, joint and conditional probabilities arealso appropriate. Combinations and modifications of the previouslymentioned examples are understood to be within the scope of the presentinvention. Heuristic methods may be applied as the database expands.

The information collected from non-placental biological samples may beused to estimate for each biomarker the diagnostic content or value.Usually, however, this diagnostic value of only one biomarker is toosmall to get a highly accurate diagnosis with accuracy rates,specificities and sensitivities beyond the 90% barrier. It may be notedthat the diagnostic content for suitable miRNAs can be found in thetables in FIGS. 2 and 5, described below. These tables includes themiRNAs with the sequences, and the significance value as computed by at-test and further statistical measures.

In one embodiment, the invention comprises a method for diagnosing adisease or condition, comprising the steps (1) quantifying miRNAs withina predetermined miRNA profile in a non-placental biological sample froman individual (patient or subject); and (2) comparing said miRNA profileto a reference, wherein the reference is the set of quantifications ofsaid miRNA profile of one or the average of many individuals that arewithout disease or have a second condition to which the first conditionis to be distinguished or compared. The comparison permits diagnosis.Wherein the comparison is between two temporally separate non-placentalbiological samples of the same individual, it may be used to determineclinical progress. Wherein the two non-placental biological samples ofthe same individual span a therapeutic intervention, the relativeefficacy of therapy may be assessed.

In one embodiment, the method comprises providing a non-placentalbiological sample from a subject with a history of preeclampsia andrelated conditions or risk of such disorder or related disorder saidsample being derived from cells of the biologic sample, for example,derived from peripheral blood or bone marrow, and then isolatingmononuclear cells as taught by Boyum (Boyum A 1983. Isolation of humanblood monocytes with Nycodenz, a new non-ionic iodinated gradientmedium. Scand J Immunol 17: 429-436) and then determining the amount ofnon-coding RNA such as preferably microRNA (microRNAs) and comparing tothe amount of the corresponding RNA in the sample to similarly treatednon-placental biological sample from control individuals. In addition,the method can comprise quantification of a plurality of individualmicroRNAs from the non-placental biological sample and quantifying theindividual microRNAs and comparing the amount of microRNAs tocorresponding microRNA control levels. The subject is then diagnosed ashaving preeclampsia and related conditions or risk of developing such adisorder if there is differential expression in the amount of one ormore of the RNAs from the sample as compared to corresponding RNAcontrol levels. In some embodiments, the method further comprisesselecting a treatment or modifying a treatment based on the amount ofthe one or more RNAs determined. This determination may be based uponassessment of specific individual or combinations of the individualmicroRNAs.

Methods for quantifying or semi-quantifying microRNA are well known inthe art. These include but are not limited to nucleic acid hybridizationtechniques well known in the art for example performed using a solidphase support comprising specific, bound polynucleotides complementaryto the target microRNA sequence. RNA isolated from a biologic sample maybe reversed transcribed into DNA and conjugated with a detectable labeland thence contacted with the anchored probes under hybridizingconditions and scanned by a detection system permitting discretequantification of signals. It is understood that probe sequences mayalso be complementary to target sequences comprising SNPs. Moreover, itis understood that probe sequences may be complementary to pre-microRNAand pri-microRNA regions of specific microRNAs. Techniques comprisingthe polymerase chain reaction, preferably those incorporating real timetechniques wherein amplification products are detected through labeledprobes or utilizing non-specific dye amplicon-binding dyes such as cybergreen.

Further, RNA may be extracted from cells isolated cells selected by saidmeans may be prepared by extraction according to instructions from themanufacturer (Qiagen catalogue 763134). microRNA such as for mir-155 maybe detected and quantified by PCR (polymerase chain reaction) by themethod described by Chen et al.(http://www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_040548.pdf downloaded 5/11/10). Primers and reagents may beselected for individual microRNAs from those described in productoverview(http://www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_068884.pdfdownloaded 5/11/10). This document provides information teaching thedetection and quantification of individual microRNAs.

In one aspect of the invention, an expression profile of a predeterminedset of miRNAs is identified. It is understood that expression profilesmay consist of the entirety of all known microRNAs incorporated into amicroarray chip. Any of several methods may be used for quantificationor semi-quantification. Determination of an expression profile may beperformed by quantitative or semi-quantitative determination of a panelof microRNAs in patients affected by a condition to be assessed and inindividuals without said condition. Alternatively, determination of anexpression profile that may be used to determine progress of a conditionmay be determined in a similar manner wherein comparison is made byquantitative or semi-quantitative differences between the two timepoints. Separate expression profiles may be determined in a similarmanner wherein the two time points are separated by a therapeuticintervention. In a similar manner individual expression profiles may bedetermined at different time points particularly during the course ofpregnancy including time points within 6 months preceding or followingpregnancy by a term of approximately six months. Panels of microRNAs tobe assessed selected a priori or they may comprise large collectionsintended to include all currently known microRNAs such as in amicroarray. The determination may be carried out by any means fordetermining nucleic acids.

Selective interrogation of subsets of cells of the non-placentalbiological sample can impart additional information. In a preferredembodiment, any leukocyte population, for example, monocytes,lymphocytes, or granulocyte or cell fragments such as platelets, may besegregated by means well known in the art permits selectivequantification of microRNAs within that cell population. Further, forexample, lymphocyte subpopulations, themselves, can be individuallyinterrogated following their selective isolation by such techniques, forexample, flow cytometric sorting following interaction withfluorescently labeled monoclonal antibody combinations that are capableof discreetly characterizing the individual subclasses. For example, Tregulatory cells may be contacted under selective binding conditionswith fluorescently labeled anti-CD3, CD4, CD25 and CD127 and selected bytheir expression of CD3, CD4, CD25 and absence or low expression ofCD127.

A novel aspect of the present invention is the separation of patientsinto groups distinguishable by characteristic changes in single ormultiple microRNAs following the selected therapeutic intervention.Identification of patients belonging to microRNA response groups isassociated with improved efficacy, prognosis and utility of particulartherapeutic intervention(s). Moreover, quantitative levels of certainmicroRNAs and patterns of change within microRNAs may predict patientresponse group(s) and post-therapy levels may have additional predictivevalue. Use of microRNA patterns responsive to therapeutic interventionor predictive thereof provides useful insights into managementunavailable through identification of markers directly related to thepathologic process.

Techniques for assessing microRNA may general include those thatquantify or semi-quantify total microRNA in a biologic specimen.Alternatively, techniques such as in situ hybridization using probesspecific for individual microRNAs may be used. These techniques permitidentification of individual cells comprising the quantified microRNA.In one embodiment these techniques involve the use of multiple probes. Afirst set comprising one or a plurality of uniquely labeled probes maybe used to identify cells. A second set of uniquely labeled probesmicroRNA-complementary probes may be used to quantify specific microRNAscomprised by said cell. Probes used to identify a cell may be monoclonalantibodies conjugated to fluorescent dyes. Probes used to quantifymicroRNAs comprised by said cells are conjugated to unique fluorescentdyes. Preferably probes are nucleic acid sequences complementary toselected regions of the microRNA or portions of the precursors of themicroRNA. The probes may be comprised of synthetic polynucleotidesequences such as locked-nucleic acids. Detection methods may compriseflow cytometry or image cytometry. These techniques are advantageous.They can, for example, distinguish total microRNA levels in a biologicsample into patterns wherein relatively small quantities of a microRNAare broadly expressed amongst cells within a biologic sample or largequantities expressed within a selected and identifiable small populationof cells within a biologic sample. It is understood that in situhybridization techniques may also be used on suitably prepared tissuesamples. Techniques are known and practicable by those of ordinary skillin the art.

Prior to analysis of a non-placental biological sample, nucleic acidsuch as microRNA generally may be isolated. In embodiments including insitu hybridization, the cellular or tissue structure may be left intact.Steps employed comprise a number of separable operations that includeconcentration, suspension, extraction of intracellular material whereinthese steps are well known to those of ordinary skill in the art.Numerous commercially available kits that comprise reagents andinstructions are available and are specifically designed for efficientisolation of RNA of the small size of miRNA.

Two exemplary methods for isolating RNA include phenol-based extractionand silica matrix or glass fiber filter (GFF)-based binding.Phenol-based reagents comprise various components that denaturantssample constituents, possess the capacity to inhibit RNase's that permitcell and tissue disruption that is followed by steps that permitseparation of the RNA from other constituents of the sample. Commercialreagents and kits may be configured to recover short RNA polynucleotidesof microRNA length. Extraction procedures such as those using Trizol orTriReagent are useful wherein both long and short RNA polynucleotidesare desired. Any method is within the scope of this invention.

It is understood herein that detection of miRNA may include detection ofthe presence or absence of a specific microRNA within a non-placentalbiological sample, and more preferably its quantification. The methodsmay produce quantitative or semi-quantitative results. It is understoodthat relative quantification wherein comparative levels between thesample of the patient is related to the level in a control or othersample particularly wherein sequential samples are assayed. Anydetection method well known to those skilled in the art falls within thescope of the invention. Hybridization, preferably where a polynucleotidecomplimentary to the target polynucleotide is labeled, may be used todetect the target strand. The polymerase chain reaction incorporatinglabeled probes, electrophoresis or other detection strategy may beemployed. Sequencing of target strands may also be used.

It is apparent from the gene structure incorporating a small non-codingRNA that they are affected in similar ways to those geneprotein-encoding genes. For example, single nucleotide polymorphisms(SNPs) are well-described variants identified within genes and affecttheir translation and stability. In a similar manner, SNPs affecttranslation and functionality of small non-coding RNAs. These variationsmay be within the small non-coding RNA itself or within the adjacentregions within the gene or nearby regions that affect theirtranscription as well as corresponding SNPs in the target mRNA. Itappears that the number of human microRNAs may exceed 1000. It isunderstood that interrogation of microRNAs and their precursorsincluding nearby sequences that may affect their transcription withprobes and primers specific for SNPs are within the scope of theinvention.

Single nucleotide polymorphisms (SNP) within both microRNAs and theirflanking regions and target mRNAs may alter target specificity resultingin loss or diminished effect between wild-type species andSNP-comprising counterparts. Further such polymorphisms may generate newmRNA targets interactions. These polymorphisms may be result in alteredefficiency of microRNA regulation of target mRNAs. Further,Polymorphisms may potentially affect microRNA-mediated regulation of thecell can be present in the 3′-UTR of a microRNA target gene. Additionalpolymorphisms may also be present in the genes involved in microRNAbiogenesis as well as in pri-, pre- and mature-microRNA sequences. Theconsequences of such polymorphisms in processed microRNAs may haveprofound effects on the expression of a multiplicity of target genes andhave serious consequences, whereas a polymorphism in microRNA targetsite, in the 3′-UTR of the target mRNA, may be more target and/orpathway specific.

Nucleic acid characterization and quantification are used to assess theprobability of success of a particular therapeutic intervention. It isthe goal of personalized medicine to identify patients whom are likely,or conversely unlikely to respond to a candidate therapy. Cost,side-effects and improved therapeutic response are accepted reasons forpursuing nucleic acid testing as a means of selecting therapies and forfollowing the course of therapy. Not only might quantification ofmicroRNAs be helpful in identifying patients suffering from, but suchquantifications would be of corresponding assistance in selecting anddirecting therapeutic choices and monitoring their effects in avirtually unlimited variety of disorders.

In one embodiment, an individual of ordinary skill in the art using ahuman microRNA array from Agilant Technologies (for example cat.G4471A-029297) and following the directions of the manufacturer)quantifies all known human microRNAs on specimens RNA extracted fromcells of the non-placental biological sample according to instructionsof the microarray manufacturer. Blood collected is drawn intoheparinized tubes and maintained at room temperature preferably forapproximately 24 hours prior to isolation of cells. RNA sampling andextraction: cells or sorted cell populations (<1×10{circumflex over( )}7 viable cells) were collected in 1 ml TRIzol (Invitrogen) andstored at −80c until use). Total RNA was isolated according to theTRIzol protocol (Invitrogen) or RNeasy Mini Kit (Qiagen). For using theRNeasy Mini Kit, the entire procedure was carried out at roomtemperature with the QIAcube automated robot (Qiagen). Total RNA yieldwas assessed using the Thermo Scientific NanoDrop 1000 micro-volumespectrophotometer (absorbance at 260 nm and the ratio of 260/280 and260/230). RNA integrity was assessed using the Agilent's BioanalyzerNANO Lab-on-Chip instrument (Agilent). MicroRNA Microarray processing.The microRNA microarray data was normlized by using the Agilent'sGeneSpring GX v11.5.1 (see the link)(http://www.chem.agilent.com/en-US/Products-Services/Software-Informatics/GeneSpring-GX/pages/default.aspx)downloaded Oct. 7, 2012.

Accordingly, embodiments of the invention may include the use of atleast one microRNA selected from hsa-let-7e, mir-1, hsa-mir-1181,hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p, hsa-mir-1296,hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141, hsa-miR-142-3p,hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-miR-154,hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p, hsa-mir-29b,hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32, hsa-mir-33a,hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, hsa-377, hsa-miR-423-3p,hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545, hsa-miR-548a-5p,hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-15a,hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21, hsa-mir-1237,hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3p andhsa-mir-423-5p. In another aspect, the microRNA is selected fromhsa-mir-136, hsa-mir-141, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153,hsa-mir-1537, hsa-mir-193a-3p, hsa-mir-219-5p, hsa-mir-29b,hsa-mir-301a, hsa-mir-32, hsa-mir-33a, hsa-mir-545, hsa-mir-582-3p,hsa-mir-590-5p, hsa-mir-1181, hsa-mir-513a-5p, hsa-mir-132 andhsa-mir-1296. In another aspect, the microRNA is selected fromhsa-miR-144, hsa-miR-582-5p, hsa-miR-30e-3p, hsa-miR-340-5p,hsa-miR-424-5p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210,hsa-miR-221-5p, hsa-miR-33a-5p, hsa-miR-575, hsa-miR-7-5p, hsa-miR-1229,hsa-miR-1267, hsa-miR-671-3p, hsa-miR-1244, hsa-miR-1 and hsa-miR-133b.In another aspect, the at least one microRNA may be mir-1229 ormir-671-3p. In yet another aspect, quantifying and comparing may includeusing at least four microRNAs selected from the group consisting ofmiR-7-5p, miR-1229, miR-1267, miR-671-3p, miR-340-5p, hsa-miR-1,hsa-miR-133b and hsa-miR-33a-5p.

Using the techniques described above, a number of microRNAs may beidentified and evaluated based on their relative expression in patientsexperiencing a preeclampsia-related condition and may be compared tocorresponding expression levels of such microRNAs in patient exhibitinga normal pregnancy. Details regarding the identification and expressionof exemplary micoRNAs are given in FIGS. 1-22, as described below.

FIG. 1 lists 30 microRNAs selected as demonstrating potential forpredicting pregnancy outcome based on pregnancy outcome data asdescribed in PCT/US12/61994 filed Oct. 25, 2012. Accordingly, in oneembodiment, at least one microRNA may be selected from the groupconsisting of hsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267,hsa-miR-132, hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a,hsa-miR-148a-3p, hsa-miR-155, hsa-miR-16, hsa-miR-181a, hsa-miR-193a-3p,hsa-miR-196a, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210,hsa-miR-219-5p, hsa-miR-221-5p, hsa-miR-223, hsa-miR-301a-3p,hsa-miR-30e-3p, hsa-miR-33a-5p, hsa-miR-340-5p, hsa-miR-424-5p,hsa-miR-513a-5p, hsa-miR-hsa-miR-575, hsa-miR-582-5p, hsa-miR-671-3p andhsa-miR-7-5p.

FIG. 2 shows the differences in preconception microRNA levels betweenHealthy and Preeclampsia patients of the microRNAs of FIG. 1. The 30selected microRNAs CT levels (The term “CT” in real time PCR means thecycle threshold number at which the amplified PCR product becomesdetectable) were measured at a single preconception blood draw (mean60.7±53.6 days before the conception cycle LMP (last menstrual period)day) for each of 29 patients (14 preeclampsia patients, 15 healthypatients). Mean preeclampsia and healthy microRNA CT level differencesare sorted from highest to lowest. The absolute value of the miR CTdifferences were divided by average SD (average SD means the sum of thestandard deviations (SD) divided by 2). The most predictive microRNAs ofpregnancy outcome have a value ≥1 (marked with X). Based on these data,miR-223, miR-7-5p, miR 148a-3p, miR-144-3p, miR7-5p, miR-16 and 582-5pwere found to be the most useful preconception microRNAs for predictingpregnancy outcome of the 30 microRNAs tested. Accordingly, in oneembodiment, at least one microRNA may be selected from the groupconsisting of.

FIG. 3 shows differences in preconception microRNA levels between MostHigh Risk Outcome Group and Lowest Risk Outcome Group. As an additionalanalysis, the differences between the “Most high risk group”(Preeclampsia despite using IVIG at a positive pregnancy test) and the“Lowest risk group” (Healthy outcome despite no IVIG used at a positivepregnancy test) were calculated. The mean preconception values of thesetwo groups were sorted from highest to lowest. Significant microRNAs hada difference between mean preeclampsia and mean healthy levels with anabsolute value difference set at ≥1.0 (marked with XX). It is notablethat miR 144-3p, 148a-3p and 582-5p are again on the top list ofmicroRNA preconception predictors, similar to results in FIG. 2.Accordingly, in one embodiment, at least one microRNA may be selectedfrom the group consisting of hsa-miR-144-3p, hsa-miR-148a-3p,hsa-miR-582-5p, hsa-miR-301a-3p, hsa-miR-146a, hsa-miR-575,hsa-miR-199a-5p, hsa-miR-133b and hsa-miR-424-5p.

FIG. 4 shows results associated with first trimester pregnancy testing.The mean differences between first trimester preeclampsia and healthymiR CT single levels at 30-85 days pregnant (mean 60.6±15.0 days) werecalculated. No IVIG was used with these samples. The miR CT differencesare sorted from largest to smallest. The most significant microRNAs foroutcome prediction from early pregnancy were set at an absolute valuedifference ≥1.0 (marked XXX). The top five microRNAs (highlightedmicroRNAs) were included in one exemplary scoring system for predictingpreeclampsia risk in early pregnancy, as discussed below with regard toFIG. 11. Accordingly, in one embodiment, at least one microRNA may beselected from the group consisting of hsa-miR-210, hsa-miR-1229,hsa-miR-223, hsa-miR-575 and hsa-miR-340-5p.

FIG. 5 shows differences between sequential microRNA levels in earlypregnancy between Preeclampsia and Healthy pregnancies (no IVIG). Inaddition to single pregnancy blood draws, sequential blood draws wereinvestigated to observe patterns of microRNA change in response topregnancy. Sequential microRNA levels with two blood draws were trackedat approximately 22 days apart. Pregnancy blood draw #1 and draw #2levels (second blood draw subtract first blood draw) were sorted bypreeclampsia and healthy differences in untreated samples. The mean dayof first blood draw was 50.2±20.1 days pregnant. The mean day of secondblood draw was 72.0±21.7 days pregnant. The microRNA levels are sortedfrom most increased to most decreased level. The most significantmicroRNAs were set at an absolute value mean difference ≥1.5 (designatedby *). These data may help determine which changes in microRNA levelspredict pregnancy outcome without contamination from a therapy effect.Accordingly, in one embodiment, at least one microRNA may be selectedfrom the group consisting of hsa-miR-1229, hsa-miR-146a, hsa-miR-210,hsa-miR-1244, hsa-miR-132 and hsa-miR-133b.

FIG. 6 shows differences between preeclampsia and healthy microRNA levelchanges, such that “differences in change”) were measured. Differencesbetween Preeclampsia and Healthy changes were sorted from most increasedchange to most decreased change. This data may show how Healthy microRNA“changes” compare to Preeclampsia microRNA “changes”. This comparisoncontributes information about what microRNA movement patterns may needto be treated if a healthy pregnancy outcome is desired. Again, allpatients were not exposed to IVIG. All changes were therefore a resultof the pregnancy condition itself. With this in mind, it is notable thatmicroRNAs that move as a result of the pregnancy condition are alsomicroRNAs that respond well to IVIG as shown in FIG. 7. These dualpurpose microRNAs are marked with **. Accordingly, in one embodiment, atleast one microRNA may be selected from the group consisting ofhsa-miR-513a-5p, hsa-miR-193a-3p, hsa-miR-7-5p, hsa-miR-575,hsa-miR-221-5p, hsa-miR-133b, hsa-miR-1 and hsa-miR-199a-5.

FIG. 7 shows differences in microRNA response between preeclampsia andhealthy pregnancies with and without IVIG to detect group differences inIVIG response. In addition to observing differences in microRNA behaviorbetween preeclampsia and healthy pregnancies, differences in IVIGresponse between preeclampsia and healthy pregnancies were alsoreviewed. The deltas of healthy patients using IVIG were compared tothose not using IVIG (mean gestational age of first blood draw 62.4±18.8days; of second blood draw 86.7.0±21.4 days). The deltas of preeclampsiapatients using IVIG were then compared to those not using IVIG (meangestational age of first blood draw 69.0±18.8 days; of second blood draw97.4±40.2 days). Delta differences of patients in each outcome groupusing IVIG were compared to those not using IVIG and the results weresorted from largest difference to smallest difference. This demonstratesthe “IVIG effect” on specific microRNA CT levels with differentpregnancy outcomes. It is notable that the opposite” IVIG effect”(opposite movement of highlighted miRs) in the different outcome groups(marked with *** Healthy pregnancy decrease/preeclampsia increase and**Healthy pregnancy increase/Preeclampsia decrease). It is significantto note that many of these highlighted “IVIG effect” MicroRNAs areassociated with differential pregnancy outcome in untreated pregnanciesas discussed with regard to FIG. 6. This implies that IVIG may be usefulas a clinical tool for “correcting” or “adjusting” high risk microRNApatterns once they are diagnosed, adding to the value of the microRNAtest as treatment monitoring tool as well as a diagnostic tool.Accordingly, in one embodiment, at least one microRNA may be selectedfrom the group consisting of hsa-miR-513a-5p, hsa-miR-193a-3p,hsa-miR-221-5p, hsa-miR-340-5p, hsa-miR-7-5p, hsa-miR-575, hsa-miR-1,hsa-miR-199a-5p and hsa-miR-33a-5p.

FIG. 8 correlates selected microRNAs associated with poor pregnancyoutcomes with IVIG response. As shown, column A identifies mircroRNAsexhibiting the most disregulated results of pathologic microRNA patternsassociated with poor pregnancy outcome and column B includes the IVIGresponse data from FIG. 7, that may indicate potential for treatment.Accordingly, in one embodiment, at least one microRNA may be selectedfrom the group consisting of hsa-miR-513a-5p, hsa-miR-193a-3p,hsa-miR-7-5p, hsa-miR-575, hsa-miR-221-5p, hsa-miR-133b, hsa-miR-1 andhsa-miR-199a-5p.

FIG. 9 shows the Effect of Lymphocyte Immunotherapy (LIT) onpreconception levels of selected miroRNAs. In addition to the microRNAIVIG response, LIT is a preconception therapy used to increase fertilityrates and to decrease miscarriage and preeclampsia rates. Because LIT isa preconception treatment, only preconception samples were compared inthis analysis. Mean microRNA CT levels in 15 patients usingpreconception LIT (mean day of blood draw 81.7±55.6 days beforeconception LMP) were compared to the mean levels in 10 patients notusing LIT (mean day of blood draw 62.9±54.9 days before conception LMP).LIT was performed a mean of 31.5±16.6 days before the sample was taken.Differences between mean microRNA levels of patients using LIT and notusing LIT were calculated. These results were sorted from largestdifference to smallest difference. Standard deviations were calculatedfor the mean differences between these groups. If the mean differencewas found to be ≥1.0 and the standard deviation was found to be <4.0(due to large SDs seen with preconception samples), the result wasmarked as significant (marked with XX). Using these criteria, 3preconception microRNA CT levels were found to be significantlyincreased by LIT: miR-575, 148a-3p and 144-3p. Four preconceptionmicroRNA CT levels were found to be decreased by LIT: miR-210, 193-3p,199a-5p and 199b-5p. It may be noted that high miR-148-3p and 144-3plevels are seen to be associated with negative pregnancy outcome (c.f.,FIGS. 2 and 3). This implies that LIT may be useful as a clinical toolfor “correcting” microRNA patterns before a patient actually becomespregnant. Accordingly, in one embodiment, at least one microRNA may beselected from the group consisting of hsa-miR-575, hsa-miR-144-3p,hsa-miR-148a-3p, hsa-miR-210, hsa-miR-193a-3p, hsa-miR-199b-5p andhsa-miR-199a-5p.

FIG. 10 shows an exemplary scoring system as applied to selectedmircoRNAs. Suitable “cut-off” values for the selected microRNAs may beused for predicting pregnancy outcome. By combining the predictivevalues of individual microRNAs, a more predictive scoring algorithm maybe provided. To create this algorithm, first, 28 samples from earlypregnancy (mean 60.6±15.0 days gestational age) were collected, 11,preeclampsia and 17 healthy. The absolute values of the mean differencesbetween outcome groups were calculated, as described above for FIG. 4,identifying five microRNAs were selected as “top scorers” (marked xxx).Each of these microRNAs was assigned a “cut off” value for scoring basedon a receiver operating characteristic (ROC) curve analysis wherebyfrequency of the outcome at the top third or bottom third of thepopulation range determined a cut-off level (in this population, thesample preeclampsia: healthy pregnancy ratio was 1:3). Light fontrepresents a healthy pregnancy microRNA CT value. Bold font represents apreeclampsia pregnancy CT value. Starred cells represent CT values abovethe designated 1/3 level cut-off value. “Pree” indicates preeclampsiaand “Heal” indicates healthy.

FIG. 11 shows microRNA significance as scored based on the frequency ofthe presence of the microRNA in the above figures. Accordingly, in oneembodiment, at least one microRNA may be selected from the groupconsisting of the 11 most frequently occurring microRNAs and may includemiR575, miR144-3p, miR199a-5p, miR210, miR1229, miR133b, miR148a-3p,miR193a-3p, miR7-5p, miR223 and miR340-5p.

FIGS. 12a and 12b show a microRNA pregnancy outcome predictor scoringsystem applied to 28 pregnancy microRNA samples drawn in the firsttrimester (17 Healthy and 11 Preeclampsia). A microRNA CT level fallingabove or below the “cut-off” value determined whether a patient got apreeclampsia risk factor point (shaded cells). 11 preeclampsia samplesand the 17 healthy pregnancy samples were scored. Mean time of blooddraw was confined to 30-85 days pregnant (mean 60.5±15.2 days). Thetotal preeclampsia risk factor points were added together for each ofthe 27 patients tested. Total score ranged from 0 to 6. A higher scorecorrelates with a higher risk of preeclampsia. A lower score correlateswith a lower risk of preeclampsia. A risk factor score ≥5 indicated veryhigh preeclampsia risk (5 of 5 samples). A risk factor score ≤2indicated a very low risk of preeclampsia (1 of 12 samples). ROC curveanalysis confirmed that scoring system was predictive and valid (areaunder the ROC curve: 0.894). Accordingly, in one embodiment, at leastone microRNA may be selected from the group consisting of hsa-miR-1229,hsa-miR-210, hsa-miR-223, hsa-miR-30e-3p, hsa-miR-340-5p andhsa-miR-575.

FIG. 13 shows exemplary ROC curve calculations for analysis of themicroRNA preeclampsia scoring system. Two sets of patient data wereanalyzed: 1. Multiple blood draws per patient; 2. Single blood draw perpatient. Each table represents one ROC curve analysis, one for each setof data (Group 1, 28 Samples, No IVIG, mean 60.5±15.2 days preg andGroup 2, 19 samples, No IVIG, mean 54.5±13 days preg). To calculate theROC curve scores, two outcomes were used: P (Preeclampsia) and H(Healthy). These two outcomes are arranged by Preeclampsia Risk FactorScore. The ROC curve analyses are shown in FIG. 14.

FIGS. 15-22 show additional data from co-pending PCT/US12/61994 filedOct. 25, 2012.

FIGS. 15a and 15b show IVIG response data and FIGS. 16a and 16b showspregnancy outcome data. As shown, A=IVIG “good responder,” B=IVIG “poorresponder,” H=Healthy pregnancy outcome, P=Preeclampsia outcome,M=Miscarriage outcome, + In Top 25 highest level group out of 962 miRsfor designated pregnancy outcome, − In Bottom 25 lowest level group outof 962 miRs for designated pregnancy outcome, T Sequential pregnancysamples show increasing level and ↓ Sequential pregnancy samples showdecreasing level.

FIG. 17 shows 15 top microRNA marker candidates obtained from microarrayanalysis of 962 microRNAs. Accordingly, in one embodiment, at least onemicroRNA may be selected from the group consisting of hsa-miR-144-3p,hsa-miR-582-5p, hsa-miR-30e-3p, hsa-miR-340-5p, hsa-miR-424-5p,hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-210, hsa-miR-221-5p,hsa-miR-33a-5p, hsa-miR-575, hsa-miR-7-5p, hsa-miR-1229, hsa-miR-1267and hsa-miR-671-3p.

FIG. 18 shows an exemplary microRNA pregnancy outcome predictor.

FIGS. 19a and 19b show 19 top microRNA marker candidates obtained frommicroarray analysis of 893 microRNAs for Group A and B IVIG responsedata combined with microRNA pregnancy outcome data (column H). Column Ashows MicroRNA Group AB IVIG response ranking, Column B shows A mostincreased and B most decreased divergent movers from most extreme 100 ofeach group, Column C shows A most decreased and B most increaseddivergent movers from most extreme 100 of each group, Column D showsCommon same direction movers of increasing A and increasing B groupsselected from top 100 of each, Column E shows Common same directionmovers of decreasing A and decreasing B groups selected from bottom 100of each, Column F shows Before IVIG samples: high level (>1) miRs thatpredict group A or B behavior, Column G shows After IVIG samples: highlevel (>1) miRs that predict group A or B behavior, and Column H showsPregnancy outcome data with Most increased ↑ and most decreased 1 commondivergent movers from top 25's of both outcome groups, healthy (H),preeclampsia (P) or miscarriage (M). Accordingly, in one embodiment, atleast one microRNA may be selected from the group consisting ofhsa-miR-1181, hsa-miR-1296, hsa-miR-132, hsa-miR-136, hsa-miR-141,hsa-miR-142-5p, hsa-miR-144, hsa-miR-153, hsa-miR-1537, hsa-miR-193a-3p,hsa-miR-196a, hsa-miR-219-5p, hsa-miR-29b, hsa-miR-301a, hsa-miR-32,hsa-miR-33a, hsa-miR-545, hsa-miR-582-3p and hsa-miR-590-5p.

FIGS. 20a and 20b show selected microRNAs having significant pregnancyoutcome prediction derived from a 962 microRNA microarray using 12patient outcomes (6 healthy, 3 preeclampsia, 3 miscarriage). As shown, ↑indicates Most increased, ↓Most decreased, H=Healthy, P=Preeclampsia,M=Miscarriage, and Column A identifies the microRNAs, Column B shows(XX) H↓P↑ from top 25s and (X) H↓P↑ from top 100s, Column C shows (XX)H↑ M↓ from top 25s and (X)H↑ M↓ from top 100s, Column D shows (XX) H↑ M↓from top 25s and (X) H↑ M↓ from top 100s, and Column E shows miR GroupA-B behavior category in response to IVIG (XX) from top 25s and (X) fromtop 100s.

FIG. 21 shows the top 25 differences between mean Healthy level and meanPreeclampsia and miscarriage levels before IVIG, from a 962 microRNAsmicroarray. FIG. 22 shows the bottom 25 differences between mean Healthylevel and mean Preeclampsia and miscarriage levels before IVIG from a962 microRNAs microarray.

Described herein are presently preferred embodiments. However, oneskilled in the art that pertains to the present invention willunderstand that the principles of this disclosure can be extended easilywith appropriate modifications to other applications.

1-22. (canceled)
 23. A method comprising: (a) quantifying the expression of one or more microRNAs (miRNAs) in one or more maternal blood cells of a pregnant human being, the one or more miRNAs being selected from the group consisting of hsa-miR-148a-3p, hsa-miR-210, hsa-miR-301a-3p, hsa-miR-223, hsa-miR-340-5p, hsa-miR-575, hsa-miR-181a and hsa-miR-16; (b) comparing the expression of the four miRNAs determined in step a) to the expression of the same miRNAs in a control sample to determine whether the pregnant human being is at risk of developing a preterm birth, wherein an increase in expression in the pregnant human being relative to the control sample indicates the pregnant human being is at risk of developing a preterm birth; and, (c) treating a pregnant human being identified in step b) as being at risk of developing a preterm birth using immunotherapy.
 24. The method of claim 23 further comprising quantifying the expression of at least one miRNA selected from the group consisting of hsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267, hsa-miR-132, hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a, hsa-miR-155, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-219-5p, hsa-miR-221-5p, hsa-miR-30e-3p, hsa-miR-33a-5p, hsa-miR-424-5p, hsa-miR-513a-5p, hsa-miR-582-5p, hsa-miR-671-3p, hsa-miR-7-5p, hsa-miR-132, hsa-miR-136, hsa-miR-141, hsa-miR-144, hsa-miR-153, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-219-5p, hsa-miR-301a, hsa-miR-32, hsa-miR-33a, hsa-miR-545, hsa-miR-582-3p, and hsa-miR-590-5p.
 25. The method of claim 23 wherein the one or more maternal blood cells are obtained during the first trimester of pregnancy.
 26. The method of claim 24 wherein the one or more maternal blood cells are obtained during the first trimester of pregnancy.
 27. A method comprising: (a) quantifying the expression of one or more microRNAs (miRNAs) in one or more maternal blood cells of a pregnant human being, the one or more miRNAs being selected from the group consisting of hsa-miR-148a-3p, hsa-miR-210, hsa-miR-301a-3p, hsa-miR-223, hsa-miR-340-5p, hsa-miR-575, hsa-miR-181a and hsa-miR-16. (b) comparing the expression of the four miRNAs determined in step a) to the expression of the at least one miRNA in a control sample to determine whether the pregnant human being is at risk of having a preeclampsia, wherein an increase in expression in the pregnant human being relative to the control sample indicates the pregnant human being is at risk of having preeclampsia; and, (c) treating a pregnant human being identified in step b) as being at risk of having preeclampsia using immunotherapy.
 28. The method of claim 27 further comprising quantifying the expression of at least one miRNA selected from the group consisting of hsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267, hsa-miR-132, hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a, hsa-miR-155, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-219-5p, hsa-miR-221-5p, hsa-miR-30e-3p, hsa-miR-33a-5p, hsa-miR-424-5p, hsa-miR-513a-5p, hsa-miR-582-5p, hsa-miR-671-3p, hsa-miR-7-5p, hsa-miR-132, hsa-miR-136, hsa-miR-141, hsa-miR-144, hsa-miR-153, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-219-5p, hsa-miR-301a, hsa-miR-32, hsa-miR-33a, hsa-miR-545, hsa-miR-582-3p, and hsa-miR-590-5p.
 29. The method of claim 27 wherein the one or more maternal blood cells are obtained during the first trimester of pregnancy.
 30. The method of claim 28 wherein the one or more maternal blood cells are obtained during the first trimester of pregnancy.
 31. A method comprising: (a) quantifying the expression of one or more microRNAs (miRNAs) in one or more maternal blood cells of a pregnant human being, the one or more miRNAs being selected from the group consisting of hsa-miR-223, hsa-miR-16 and hsa-miR-575. (b) comparing the expression of the four miRNAs determined in step a) to the expression of the at least one miRNA in a control sample to determine whether the pregnant human being is at risk of having a miscarriage, wherein an increase in expression in the pregnant human being relative to the control sample indicates the pregnant human being is at risk of having a miscarriage; and, (c) treating a pregnant human being identified in step b) as being at risk of having a miscarriage using immunotherapy.
 32. The method of claim 31 further comprising quantifying the expression of at least one miRNA selected from the group consisting of hsa-miR-148a-3p, hsa-miR-210, hsa-miR-301a-3p, hsa-miR-1, hsa-miR-1229, hsa-miR-1244, hsa-miR-1267, hsa-miR-132, hsa-miR-133b, hsa-miR-144-3p, hsa-miR-146a, hsa-miR-155, hsa-miR-181a, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-219-5p, hsa-miR-221-5p, hsa-miR-30e-3p, hsa-miR-33a-5p, hsa-miR-340-5p, hsa-miR-424-5p, hsa-miR-513a-5p, hsa-miR-582-5p, hsa-miR-671-3p, hsa-miR-7-5p, hsa-miR-132, hsa-miR-136, hsa-miR-141, hsa-miR-144, hsa-miR-153, hsa-miR-193a-3p, hsa-miR-196a, hsa-miR-219-5p, hsa-miR-301a, hsa-miR-32, hsa-miR-33a, hsa-miR-545, hsa-miR-582-3p, and hsa-miR-590-5p.
 33. The method of claim 31 wherein the one or more blood maternal cells are obtained during the first trimester of pregnancy.
 34. The method of claim 32 wherein the one or more maternal blood cells are obtained during the first trimester of pregnancy. 